feat: 第四轮大版本更新 — 修复4个严重Bug、2个UI Bug，实现自主思考重构与主-子会话架构
## 🐛 Bug 修复 - 修复前端对话无响应：消除 ChatContainer 中的双重 WebSocket 连接，优化 sendMessage 失败提示 - 修复 Memory-Service 数据库迁移失败：ai-core 和 memory-service 均添加 ALTER TABLE ADD COLUMN IF NOT EXISTS 模式演化 - 修复语音/STT 不可用：添加 MediaRecorder API 降级方案，修复 whisper-cli 输出文件名错误 - 修复仪表盘数据库按钮失效：补充按钮 ID 属性，重写 controlDB() 控制逻辑 ## 🎨 UI 修复 - 修正用户消息头像位置：从 flex-row-reverse 改为 justify-end - 移除空聊天列表的 emoji 占位图标 ## ✨ 新功能 - devtools 新增 STT 处理日志面板（环形缓冲区 + WebSocket 广播 + 可视化表格） - 新增 ADMIN_NICKNAME 环境变量，支持自定义管理员昵称 ## 🔧 改进 - 注册流程增加昵称必填字段（前后端同步） ## 🏗️ 架构重构 - 重构自主思考逻辑：从定时器轮询改为事件驱动（对话后触发 + 静默检测），优化提示词使其更自然人性化 - 实现主-子会话架构：新增 4 种子会话类型（general/memory/iot/knowledge），意图分析 → 并行分发 → 结果合成流程 ## 📄 新增文档 - docs/architecture/main-session-sub-session-design.md — 子会话架构设计文档
This commit is contained in:
+89
-178
@@ -21,9 +21,12 @@ import (
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"github.com/yourname/cyrene-ai/ai-core/internal/model"
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"github.com/yourname/cyrene-ai/ai-core/internal/orchestrator"
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"github.com/yourname/cyrene-ai/ai-core/internal/persona"
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"github.com/yourname/cyrene-ai/ai-core/internal/subsession"
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"github.com/yourname/cyrene-ai/ai-core/internal/tools"
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)
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var cfg Config
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func main() {
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// 自动加载 .env 文件（来自 backend/.env）
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if err := godotenv.Load("../.env"); err != nil {
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@@ -34,7 +37,7 @@ func main() {
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log.Println("🧠 AI-Core 服务启动中...")
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// 加载配置
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cfg := loadConfig()
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cfg = loadConfig()
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// 初始化人格加载器
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personaDir := cfg.PersonaDir
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@@ -147,8 +150,29 @@ func main() {
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// 健康检查与对话API的HTTP mux
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mux := http.NewServeMux()
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// 手动构建 orchestrator 用于处理（因为现有orchestrator结构体已定义但未导出构造函数）
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orch := &orchestrator.Orchestrator{}
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// 初始化子会话管理器
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subManager := subsession.NewManager(llmAdapter)
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// 注册子会话提供者
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subManager.Register(subsession.NewGeneralProvider(personaLoader))
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if memRetriever != nil {
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subManager.Register(subsession.NewMemoryProvider(memRetriever))
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}
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if iotClient != nil {
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subManager.Register(subsession.NewIoTProvider(iotClient))
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}
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log.Printf("子会话管理器已就绪: %d 个提供者 (%v)", len(subManager.ListProviders()), subManager.ListProviders())
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// 构建新的 Orchestrator (v2.0)
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orch := orchestrator.NewOrchestrator(
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personaLoader,
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ctxBuilder,
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llmAdapter,
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subManager,
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memRetriever,
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memExtractor,
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)
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log.Println("对话编排器 v2.0 已就绪")
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// 注册对话API端点
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mux.HandleFunc("/api/v1/chat", func(w http.ResponseWriter, r *http.Request) {
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@@ -203,6 +227,7 @@ type Config struct {
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LLMFallbackModel string
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DatabaseURL string
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IoTServiceURL string
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AdminNickname string // 昔涟对管理员用户的基本称呼
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}
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func loadConfig() Config {
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@@ -215,6 +240,7 @@ func loadConfig() Config {
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LLMFallbackModel: getEnv("LLM_FALLBACK_MODEL", "gpt-4o-mini"),
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DatabaseURL: buildDatabaseURL(),
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IoTServiceURL: getEnv("IOT_DEBUG_SERVICE_URL", ""),
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AdminNickname: getEnv("ADMIN_NICKNAME", "管理员"),
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}
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}
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@@ -275,19 +301,19 @@ func buildOpenAITools(registry *tools.Registry) []llm.OpenAITool {
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return result
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}
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// handleChat 处理对话请求（SSE 流式响应 + 工具调用）
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// handleChat 处理对话请求（SSE 流式响应）— 使用新 Orchestrator v2.0
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func handleChat(
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w http.ResponseWriter,
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r *http.Request,
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_ *orchestrator.Orchestrator,
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orch *orchestrator.Orchestrator,
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ctxBuilder *ctxbuild.Builder,
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llmAdapter *llm.Adapter,
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personaLoader *persona.Loader,
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memRetriever *memory.Retriever,
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memExtractor *memory.Extractor,
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_ *llm.Adapter,
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_ *persona.Loader,
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_ *memory.Retriever,
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_ *memory.Extractor,
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iotClient *tools.IoTClient,
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thinker *background.Thinker,
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toolRegistry *tools.Registry,
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_ *tools.Registry,
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) {
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if r.Method != http.MethodPost {
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http.Error(w, "Method not allowed", http.StatusMethodNotAllowed)
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@@ -300,6 +326,7 @@ func handleChat(
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SessionID string `json:"session_id"`
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Message string `json:"message"`
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Mode string `json:"mode"`
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Nickname string `json:"nickname,omitempty"`
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}
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if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
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http.Error(w, "无效的请求体", http.StatusBadRequest)
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@@ -317,82 +344,16 @@ func handleChat(
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thinker.RecordUserMessage()
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}
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// 确定用户昵称
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userNickname := req.Nickname
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if userNickname == "" {
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userNickname = cfg.AdminNickname
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}
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// 0.1 缓存用户消息到会话历史
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ctxBuilder.CacheMessage(req.SessionID, model.RoleUser, req.Message)
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// 1. 检索相关记忆
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var memories []memory.MemoryEntry
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if memRetriever != nil {
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var err error
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memories, err = memRetriever.Retrieve(ctx, req.UserID, req.Message)
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if err != nil {
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log.Printf("[chat] 记忆检索失败: %v", err)
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}
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}
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// 2. 加载人格配置
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personaConfig, err := personaLoader.Get("cyrene")
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if err != nil {
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http.Error(w, fmt.Sprintf("加载人格失败: %v", err), http.StatusInternalServerError)
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return
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}
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// 2.1 始终获取 IoT 设备状态（去掉关键词门控，让昔涟始终了解家里的状态）
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var deviceContext string
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if iotClient != nil {
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devices := iotClient.GetDevicesForContext()
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if len(devices) > 0 {
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deviceInfos := make([]ctxbuild.DeviceInfo, 0, len(devices))
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for _, d := range devices {
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deviceInfos = append(deviceInfos, ctxbuild.DeviceInfo{
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Name: d.Name,
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Type: d.Type,
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Status: d.Status,
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Brightness: d.Brightness,
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Color: d.Color,
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Temperature: d.Temperature,
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Mode: d.Mode,
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Value: d.Value,
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Unit: d.Unit,
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Battery: d.Battery,
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})
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}
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deviceContext = ctxbuild.InjectDeviceContext(deviceInfos)
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log.Printf("[chat] 已注入 IoT 设备状态 (%d 个设备)", len(deviceInfos))
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}
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}
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// 2.2 获取待处理的后台思考
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var pendingThoughts []string
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if thinker != nil && thinker.HasPendingThoughts() {
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pts := thinker.GetPendingThoughts()
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for _, pt := range pts {
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if pt.Content != "" {
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pendingThoughts = append(pendingThoughts, pt.Content)
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}
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}
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if len(pendingThoughts) > 0 {
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log.Printf("[chat] 注入 %d 条后台思考到上下文", len(pendingThoughts))
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}
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}
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// 3. 构建对话上下文
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llmMessages, err := ctxBuilder.Build(ctx, ctxbuild.BuildParams{
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UserID: req.UserID,
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SessionID: req.SessionID,
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UserMessage: req.Message,
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Persona: personaConfig,
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Memories: memories,
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HistoryLimit: 20,
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DeviceContext: deviceContext,
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PendingThoughts: pendingThoughts,
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})
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if err != nil {
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http.Error(w, fmt.Sprintf("构建上下文失败: %v", err), http.StatusInternalServerError)
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return
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}
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// 4. 设置 SSE 响应头
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// 1. 设置 SSE 响应头
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w.Header().Set("Content-Type", "text/event-stream")
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w.Header().Set("Cache-Control", "no-cache")
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w.Header().Set("Connection", "keep-alive")
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@@ -404,57 +365,17 @@ func handleChat(
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return
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}
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// 5. 准备工具定义
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openAITools := buildOpenAITools(toolRegistry)
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// 5.1 如果启用了工具，先进行同步调用检测是否需要工具调用
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if len(openAITools) > 0 {
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log.Printf("[chat] 启用工具调用: %d 个工具可用", len(openAITools))
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syncResp, syncErr := llmAdapter.ChatWithTools(ctx, llmMessages, openAITools)
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if syncErr != nil {
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log.Printf("[chat] 工具检测调用失败: %v，降级为普通对话", syncErr)
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} else if len(syncResp.ToolCalls) > 0 {
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log.Printf("[chat] 模型请求 %d 个工具调用", len(syncResp.ToolCalls))
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// 将助手消息（含工具调用）加入上下文
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assistantMsg := model.LLMMessage{
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Role: model.RoleAssistant,
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Content: syncResp.Content,
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ToolCalls: syncResp.ToolCalls,
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ReasoningContent: syncResp.ReasoningContent,
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}
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llmMessages = append(llmMessages, assistantMsg)
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// 执行每个工具调用并将结果加入上下文
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for _, tc := range syncResp.ToolCalls {
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var args map[string]interface{}
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if err := json.Unmarshal([]byte(tc.Arguments), &args); err != nil {
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log.Printf("[chat] 工具 %s 参数解析失败: %v", tc.Name, err)
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args = make(map[string]interface{})
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}
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result, execErr := toolRegistry.Execute(ctx, tc.Name, args)
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if execErr != nil {
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log.Printf("[chat] 工具 %s 执行失败: %v", tc.Name, execErr)
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}
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resultJSON, _ := json.Marshal(result)
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llmMessages = append(llmMessages, model.LLMMessage{
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Role: model.RoleTool,
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Content: string(resultJSON),
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ToolCallID: tc.ID,
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})
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}
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}
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// 无论是否有工具调用，继续流式输出最终回复
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}
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// 5.2 调用LLM流式接口（可能已附加工具结果）
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chunkCh, err := llmAdapter.ChatStream(ctx, llmMessages)
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// 2. 调用 Orchestrator 处理（替代原有的线性处理流程）
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// Orchestrator 内部处理：意图分析 → 子会话分派 → 结果汇总 → 综合生成回复
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eventCh, err := orch.ProcessInput(ctx, orchestrator.ProcessParams{
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UserID: req.UserID,
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SessionID: req.SessionID,
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Message: req.Message,
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Mode: req.Mode,
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Nickname: userNickname,
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})
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if err != nil {
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// 流式初始化失败，返回 SSE 格式错误
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errData, _ := json.Marshal(map[string]string{"delta": "", "error": fmt.Sprintf("LLM调用失败: %v", err)})
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errData, _ := json.Marshal(map[string]string{"delta": "", "error": fmt.Sprintf("处理失败: %v", err)})
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fmt.Fprintf(w, "data: %s\n\n", errData)
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flusher.Flush()
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fmt.Fprintf(w, "data: [DONE]\n\n")
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@@ -463,66 +384,56 @@ func handleChat(
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}
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messageID := fmt.Sprintf("msg-%d", time.Now().UnixNano())
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// 6. 逐 token 推送 SSE
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var fullContent string
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var segments []llm.Segment
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segmenter := llm.NewSegmenter()
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for chunk := range chunkCh {
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if chunk.Error != nil {
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log.Printf("[chat] 流式错误: %v", chunk.Error)
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errData, _ := json.Marshal(map[string]string{"delta": "", "error": chunk.Error.Error()})
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// 3. 流式输出 SSE
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var fullContent string
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for event := range eventCh {
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switch event.Type {
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case model.StreamError:
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log.Printf("[chat] 流式错误: %v", event.Error)
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errData, _ := json.Marshal(map[string]string{"delta": "", "error": event.Error.Error()})
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fmt.Fprintf(w, "data: %s\n\n", errData)
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flusher.Flush()
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fmt.Fprintf(w, "data: [DONE]\n\n")
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flusher.Flush()
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return
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}
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if chunk.Done {
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// 流结束，flush 剩余片段
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if remaining := segmenter.Flush(); remaining != nil {
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segments = append(segments, *remaining)
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}
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break
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}
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if chunk.Content != "" {
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fullContent += chunk.Content
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// 实时断句
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newSegs := segmenter.Feed(chunk.Content)
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segments = append(segments, newSegs...)
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case model.StreamDelta:
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fullContent += event.Delta
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deltaData, _ := json.Marshal(map[string]string{
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"delta": chunk.Content,
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"delta": event.Delta,
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"message_id": messageID,
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})
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fmt.Fprintf(w, "data: %s\n\n", deltaData)
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flusher.Flush()
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case model.StreamSegments:
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// 发送断句信息
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segData, _ := json.Marshal(map[string]interface{}{
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"message_id": messageID,
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"mode": req.Mode,
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"segments": event.Segments,
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})
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fmt.Fprintf(w, "data: %s\n\n", segData)
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flusher.Flush()
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case model.StreamDone:
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// 下发结束标记
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endData, _ := json.Marshal(map[string]interface{}{
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"message_id": messageID,
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"mode": req.Mode,
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"done": true,
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})
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fmt.Fprintf(w, "data: %s\n\n", endData)
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flusher.Flush()
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fmt.Fprintf(w, "data: [DONE]\n\n")
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flusher.Flush()
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}
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}
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// 7. 发送结束标记（附带元数据）
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endData, _ := json.Marshal(map[string]interface{}{
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"message_id": messageID,
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"mode": req.Mode,
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"segments": segments,
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"done": true,
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})
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fmt.Fprintf(w, "data: %s\n\n", endData)
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flusher.Flush()
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fmt.Fprintf(w, "data: [DONE]\n\n")
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flusher.Flush()
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// 8. 缓存 LLM 回复到会话历史
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if fullContent != "" {
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ctxBuilder.CacheMessage(req.SessionID, model.RoleAssistant, fullContent)
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}
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// 9. 异步提取记忆
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if memExtractor != nil && fullContent != "" {
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go memExtractor.ExtractAndStore(context.Background(), req.UserID, req.SessionID, req.Message, fullContent)
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// 4. 对话完成后触发昔涟的自主思考（事件驱动，非定时）
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if thinker != nil {
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thinker.TriggerPostChatThink()
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}
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}
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Reference in New Issue
Block a user